<i>In Vitro</i> Biodegradation of Gliclazide by <i>Aeromonas hydrophila</i> and <i>Serratia odorifera</i> Bacteria
Bibliographic record
Abstract
Abstract Gliclazide is a pharmaceutical product used in the treatment of type 2 diabetes. However, this drug is considered to be highly undesirable when present in the environment. We tested in vitro the biodegradation of gliclazide as the sole source of carbon and energy by a microbial consortium. After a 5-month adaptation period in batch culture, two bacterial strains were isolated and identified, namely, Aeromonas hydrophila and Serratia odorifera. With an initial concentration of gliclazide at 0.5 g/L, these two bacteria and their combined culture degraded gliclazide with a specific activity of 22.3, 24.1, and 19.2 ng/(mg·h) and a yield of 88.88%, 82.94%, and 95.88%, respectively. Experimental results reveal a removal efficiency of 98.904% at an inlet concentration of 5 g/L and a flow rate of 14 L/h. The maximum removal efficiency of the biotrickling filter was 99.6%, at a gliclazide inlet concentration of 0.5, 1, and 5 g/L and a flow rate of 6.3 L/h. Interestingly, it was observed that after a period of 12 months, the two dominant strains differed from those present in the initial inocula. Thus, the high elimination efficiencies obtained in this study reveal the interest of the use, for the first time, of a biotrickling filter for the study of the biodegradation of gliclazide. Obtaining a microbial consortium strongly adapted to this substrate may prove to be an interesting alternative for a possible application in the treatment, before discharge, of wastewater containing this molecule or other related molecules, especially in the pharmaceutical industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".